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Record W4402651604 · doi:10.1051/e3sconf/202456915002

Design and use of multi-linear drainage geocomposites for gas collection layers

2024· article· en· W4402651604 on OpenAlexaff
Stéphan Fourmont, David Beamier, Lucie Benedito

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsCTT Group (Canada)Texel (Canada)
Fundersnot available
KeywordsDrainageEnvironmental sciencePetroleum engineeringComputer scienceGeology

Abstract

fetched live from OpenAlex

Pore pressures generated by gas underneath a geomembrane can affect its integrity and the entire lining system. It can create whales/hippos in a surface impoundment, significantly reduce normal stress on the lower interface and create a veneer instability on final landfill cover. The membrane is lifted by the pressure of the gas trapped beneath it. The solution to avoid such occurrences is to install a permeable material that collects and transmits the gas outside the lining system. It can be vented to the atmosphere in the case of impoundments or collected in a gas collection network for valorization in case of landfills, for example. A sand layer is certainly possible, but drainage geocomposites offer an efficient and economical alternative. Depending on the application, the drainage geocomposite is designed to act as a passive system (no mechanical vacuum applied) or active. This paper presents the use of multi-linear drainage geocomposite for gas collection and its hydraulic behavior to collect and evacuate the gas. A case study is also given with the use of geocomposite as venting layer under a lined pond.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.241
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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